Triple
T2274866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Almaty |
E50745
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | KZ 02 |
E50522
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: KZ 02 | Statement: [Almaty, vehicleRegistrationCode, KZ 02]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KZ 02 Context triple: [Almaty, vehicleRegistrationCode, KZ 02]
-
A.
KZ
chosen
KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
-
B.
KZT
KZT is the currency code for the Kazakhstani tenge, the official monetary unit of Kazakhstan.
-
C.
KAZ
KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
-
D.
UzK
UzK is the commonly used abbreviation for the University of Cologne, one of Germany’s largest and oldest universities.
-
E.
Z/28
The Z/28 is a high-performance, track-focused variant of the Chevrolet Camaro known for its enhanced handling, braking, and power.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88b05910c8190a9a2b1ff230c85f9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1ebaa148190851952567878b3a1 |
completed | March 7, 2026, 6:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71e01860819082bca15c146b9c64 |
completed | March 9, 2026, 7:08 a.m. |
Created at: March 4, 2026, 7:48 p.m.